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---
language:
- multilingual
- en
- de
- fr
- ja
license: mit
tags:
- object-detection
- vision
- generated_from_trainer
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
inference: false
datasets:
- pierreguillou/DocLayNet-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384
results:
- task:
name: Token Classification
type: token-classification
metrics:
- name: f1
type: f1
value: 0.8584
- name: accuracy
type: accuracy
value: 0.8584
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Document Understanding model (finetuned LiLT base at line level on DocLayNet base)
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) with the [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0003
- Precision: 0.8584
- Recall: 0.8584
- F1: 0.8584
- Tokens Accuracy: 0.8584
- Line Accuracy: 0.9197
## Accuracy at line level
- Line Accuracy: 91.97%
- Accuracy by label
- Caption: 79.42%
- Footnote: 68.21%
- Formula: 98.02%
- List-item: 82.72%
- Page-footer: 99.17%
- Page-header: 84.18%
- Picture: 83.2%
- Section-header: 76.92%
- Table: 97.65%
- Text: 91.17%
- Title: 77.46%
## References
### Blog posts
- Layout XLM base
- (03/05/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at line level with LayoutXLM base]()
- LiLT base
- (02/16/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at paragraph level](https://medium.com/@pierre_guillou/document-ai-inference-app-and-fine-tuning-notebook-for-document-understanding-at-paragraph-level-c18d16e53cf8)
- (02/14/2023) [Document AI | Inference APP for Document Understanding at line level](https://medium.com/@pierre_guillou/document-ai-inference-app-for-document-understanding-at-line-level-a35bbfa98893)
- (02/10/2023) [Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset](https://medium.com/@pierre_guillou/document-ai-document-understanding-model-at-line-level-with-lilt-tesseract-and-doclaynet-dataset-347107a643b8)
- (01/31/2023) [Document AI | DocLayNet image viewer APP](https://medium.com/@pierre_guillou/document-ai-doclaynet-image-viewer-app-3ac54c19956)
- (01/27/2023) [Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](https://medium.com/@pierre_guillou/document-ai-processing-of-doclaynet-dataset-to-be-used-by-layout-models-of-the-hugging-face-hub-308d8bd81cdb)
### Notebooks (paragraph level)
- LiLT base
- [Document AI | Inference APP at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)
- [Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)
- [Document AI | Fine-tune LiLT on DocLayNet base in any language at paragraph level (chunk of 512 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_paragraphlevel_ml_512.ipynb)
### Notebooks (line level)
- Layout XLM base
- [Document AI | Inference at line level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Inference APP at line level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet base dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Fine-tune LayoutXLM base on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LayoutXLM_base_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
- LiLT base
- [Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Inference APP at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
- [DocLayNet image viewer APP](https://github.com/piegu/language-models/blob/master/DocLayNet_image_viewer_APP.ipynb)
- [Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb)
### APP
You can test this model with this APP in Hugging Face Spaces: [Inference APP for Document Understanding at line level (v1)](https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v1).
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
## Model description
The model was finetuned at **line level on chunk of 384 tokens with overlap of 128 tokens**. Thus, the model was trained with all layout and text data of all pages of the dataset.
At inference time, a calculation of best probabilities give the label to each line bounding boxes.
## Inference
See notebook: [Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
## Training and evaluation data
See notebook: [Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7223 | 0.21 | 500 | 0.7765 | 0.7741 | 0.7741 | 0.7741 | 0.7741 |
| 0.4469 | 0.42 | 1000 | 0.5914 | 0.8312 | 0.8312 | 0.8312 | 0.8312 |
| 0.3819 | 0.62 | 1500 | 0.8745 | 0.8102 | 0.8102 | 0.8102 | 0.8102 |
| 0.3361 | 0.83 | 2000 | 0.6991 | 0.8337 | 0.8337 | 0.8337 | 0.8337 |
| 0.2784 | 1.04 | 2500 | 0.7513 | 0.8119 | 0.8119 | 0.8119 | 0.8119 |
| 0.2377 | 1.25 | 3000 | 0.9048 | 0.8166 | 0.8166 | 0.8166 | 0.8166 |
| 0.2401 | 1.45 | 3500 | 1.2411 | 0.7939 | 0.7939 | 0.7939 | 0.7939 |
| 0.2054 | 1.66 | 4000 | 1.1594 | 0.8080 | 0.8080 | 0.8080 | 0.8080 |
| 0.1909 | 1.87 | 4500 | 0.7545 | 0.8425 | 0.8425 | 0.8425 | 0.8425 |
| 0.1704 | 2.08 | 5000 | 0.8567 | 0.8318 | 0.8318 | 0.8318 | 0.8318 |
| 0.1294 | 2.29 | 5500 | 0.8486 | 0.8489 | 0.8489 | 0.8489 | 0.8489 |
| 0.134 | 2.49 | 6000 | 0.7682 | 0.8573 | 0.8573 | 0.8573 | 0.8573 |
| 0.1354 | 2.7 | 6500 | 0.9871 | 0.8256 | 0.8256 | 0.8256 | 0.8256 |
| 0.1239 | 2.91 | 7000 | 1.1430 | 0.8189 | 0.8189 | 0.8189 | 0.8189 |
| 0.1012 | 3.12 | 7500 | 0.8272 | 0.8386 | 0.8386 | 0.8386 | 0.8386 |
| 0.0788 | 3.32 | 8000 | 1.0288 | 0.8365 | 0.8365 | 0.8365 | 0.8365 |
| 0.0802 | 3.53 | 8500 | 0.7197 | 0.8849 | 0.8849 | 0.8849 | 0.8849 |
| 0.0861 | 3.74 | 9000 | 1.1420 | 0.8320 | 0.8320 | 0.8320 | 0.8320 |
| 0.0639 | 3.95 | 9500 | 0.9563 | 0.8585 | 0.8585 | 0.8585 | 0.8585 |
| 0.0464 | 4.15 | 10000 | 1.0768 | 0.8511 | 0.8511 | 0.8511 | 0.8511 |
| 0.0412 | 4.36 | 10500 | 1.1184 | 0.8439 | 0.8439 | 0.8439 | 0.8439 |
| 0.039 | 4.57 | 11000 | 0.9634 | 0.8636 | 0.8636 | 0.8636 | 0.8636 |
| 0.0469 | 4.78 | 11500 | 0.9585 | 0.8634 | 0.8634 | 0.8634 | 0.8634 |
| 0.0395 | 4.99 | 12000 | 1.0003 | 0.8584 | 0.8584 | 0.8584 | 0.8584 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
## Other models
- Line level
- [Document Understanding model (finetuned LiLT base at line level on DocLayNet base)](https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384) (accuracy | tokens: 85.84% - lines: 91.97%)
- [Document Understanding model (finetuned LayoutXLM base at line level on DocLayNet base)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384) (accuracy | tokens: 93.73% - lines: ...)
- Paragraph level
- [Document Understanding model (finetuned LiLT base at paragraph level on DocLayNet base)](https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512) (accuracy | tokens: 86.34% - paragraphs: 68.15%)
- [Document Understanding model (finetuned LayoutXLM base at paragraph level on DocLayNet base)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512) (accuracy | tokens: 96.93% - paragraphs: 86.55%) |